I am a fourth year graduate student studying computational biology and machine learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington with Su-In Lee. I love working on the application of machine learning to genomics and personalized health. Over the next few decades I believe these fields will have a large impact on our daily lives, and that much of this impact will be made possible by automated data analysis. Before UW I had the opportunity to study graph theory at Colorado State University, and lead research projects at Numerica for several years.
My current work focuses on actionable machine learning in both basic biology and predictive medicine in the hospital. In both areas a combination of interpretable models and transparent visualizations of the learned structure is important.
Check out my publications and blog posts for more details on my work.
Open source software
- Shap – Explains the output of any machine learning model using expectations and Shapley values. Under certain assumptions it can be shown to be the optimal linear explanation of any model’s prediction.
- ChromNet.jl – A network learning method that ingests BAM/BED files and other pre-processed data bundles (such as the one provided for all human ENCODE ChIP-seq data).
- SimplePlot.jl – A wrapper for Julia plotting based on Matplotlib. It allows natural layer based compositing and simple keyword parameter distribution to make simple plots simple and complex plots understandable.
For a full list of open source packages see GitHub
- ChromNet – An online network visualization of the chromatin network estimated from ENCODE ChIP-seq data, or custom network users upload.
- S. Lundberg, S. Lee “Consistent feature attribution for tree ensembles,” pre-print.
- S. Lundberg, S. Lee “A unified approach to interpreting model predictions,” pre-print.
- S. Lundberg, S. Lee “An unexpected unity among methods for interpreting model predictions,” presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems (best paper award).
- S. Lundberg, W. Tu, B. Raught, L. Penn, M. Hoffman, S. Lee “ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data,” in Genome Biology, 2016.
- S. Lundberg, C. Calderon, and R. Paffenroth, “Detecting Clustered Chem/Bio Signals in Noisy Sensor Feeds Using Adaptive Fusion,” in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 8393, p. 1, 2012.
- R. Nong, R. Paffenroth, S. Lundberg, and W. Leed, “Method for Lossy Compression of Point Clouds with Pointwise Error Constraints,” Patent application filed 2012.
- C. Calderon, A. Jones, S. Lundberg, and R. Paffenroth, “A data-driven approach for processing heterogeneous categorical sensor signals,” Proceedings of SPIE, vol. 8137, p. 813704, 2011.
- B. Joeris, S. Lundberg, and R. McConnell, “O (mlogn) split decomposition of strongly-connected graphs,” Discrete Applied Mathematics, vol. 158, no. 7, pp. 779–799, 2010.
- S. Lundberg, R. Paffenroth, and J. Yosinski, “Analysis of CBRN sensor fusion methods,” in Information Fusion (FUSION), 2010 13th Conference on, pp. 1–8, IEEE, 2010.
- A. Curtis, C. Izurieta, B. Joeris, S. Lundberg, and R. McConnell, “An implicit representation of chordal comparability graphs in linear time,” Discrete Applied Mathematics, vol. 158, no. 8, pp. 869–875, 2010.
- S. Lundberg, R. Paffenroth, and J. Yosinski, “Algorithms for Distributed Chemical Sensor Fusion,” Proceedings of SPIE, vol. 7698, p. 769806, 2010.
- S. Lundberg, “O (m log n) split decomposition of directed graphs,” Master’s thesis, Colorado State University, 2008.
- D. Moore, J. Stevens, S. Lundberg, and B. Draper, “Top down image segmentation using congealing and graph-cut,” in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pp. 1–4, IEEE, 2008.